How to detect the Granger-causal flow direction in the presence of additive noise?

Vinck, Martin; Huurdeman, Lisanne; Bosman, Conrado A.; Battaglia, Francesco P.; Pennartz, Cyriel M. A.; Tiesinga, Paul

doi:10.1016/j.neuroimage.2014.12.017

Cited by 87 publications

(108 citation statements)

References 63 publications

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“…Noise can affect GC estimates (Nalatore et al, 2007; Vinck et al, 2015). Fluctuating shared noise could in principle generate correlation between GC fluctuations.…”

Section: Discussionmentioning

confidence: 99%

“…Data from the same animals, partly overlapping with the data used here, have been used in several previous studies (Bosman et al, 2012; Brunet et al, 2014a, b, 2015; Pinotsis et al, 2014; Bastos et al, 2015a,b; Richter et al, 2015; Vinck et al, 2015; Lewis et al, 2016). Recordings were sampled at ∼32 kHz with a passband of 0.159–8000 Hz using a Neuralynx Digital Lynx system.…”

Section: Methodsmentioning

confidence: 99%

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Top-Down Beta Enhances Bottom-Up Gamma

et al. 2017

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Several recent studies have demonstrated that the bottom-up signaling of a visual stimulus is subserved by interareal gamma-band synchronization, whereas top-down influences are mediated by alpha-beta band synchronization. These processes may implement top-down control of stimulus processing if top-down and bottom-up mediating rhythms are coupled via cross-frequency interaction. To test this possibility, we investigated Granger-causal influences among awake macaque primary visual area V1, higher visual area V4, and parietal control area 7a during attentional task performance. Top-down 7a-to-V1 beta-band influences enhanced visually driven V1-to-V4 gamma-band influences. This enhancement was spatially specific and largest when beta-band activity preceded gamma-band activity by ∼0.1 s, suggesting a causal effect of top-down processes on bottom-up processes. We propose that this cross-frequency interaction mechanistically subserves the attentional control of stimulus selection.SIGNIFICANCE STATEMENT Contemporary research indicates that the alpha-beta frequency band underlies top-down control, whereas the gamma-band mediates bottom-up stimulus processing. This arrangement inspires an attractive hypothesis, which posits that top-down beta-band influences directly modulate bottom-up gamma band influences via cross-frequency interaction. We evaluate this hypothesis determining that beta-band top-down influences from parietal area 7a to visual area V1 are correlated with bottom-up gamma frequency influences from V1 to area V4, in a spatially specific manner, and that this correlation is maximal when top-down activity precedes bottom-up activity. These results show that for top-down processes such as spatial attention, elevated top-down beta-band influences directly enhance feedforward stimulus-induced gamma-band processing, leading to enhancement of the selected stimulus.

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“…Noise can affect GC estimates (Nalatore et al, 2007; Vinck et al, 2015). Fluctuating shared noise could in principle generate correlation between GC fluctuations.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Top-Down Beta Enhances Bottom-Up Gamma

et al. 2017

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“…We also computed GC based on the source-projected Fourier transform of time-reversed data, to distinguish "weak" asymmetries from "strong" asymmetries, as described by Haufe and coworkers (13) and Haufe et al (36). A weak asymmetry is an apparent directional interaction between a pair of network nodes, which is due to a difference in signal-to-noise ratio (SNR) across nodes (14), often caused by a linear mixture of underlying sources (37). We selected only parcel pairs for subsequent analysis for which the difference between GC and reverse GC was statistically significant (across subjects) at a P value <0.05, corrected for multiple comparisons (one-sided t test, with Bonferroni correction).…”

Section: Methodsmentioning

confidence: 99%

Frequency-specific directed interactions in the human brain network for language

Schoffelen

Hultén

Lam

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

118

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The brain's remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that the information flow between language-relevant brain areas, which is required for linguistic processing, may depend on the contributions of distinct brain rhythms.language | Granger causality | brain networks | magnetoencephalography T he human brain is capable of effortlessly extracting meaning from sequences of written or spoken words by means of a sophisticated interplay between dedicated neocortical regions. Neuroanatomical research has revealed a number of white-matter pathways that facilitate these interregional interactions (1). Electrophysiological research with electro-and magnetoencephalography (EEG/MEG) has revealed with high temporal precision the sequential activation of individual nodes embedded within the human brain network for language (2, 3). However, the nature of the functional interactions that enable the efficient flow of information between the nodes of this network has yet to be elucidated.One important feature of cortical interregional connections is that they are frequently reciprocal in nature (4), which implies that information can be exchanged in a bidirectional fashion. Moreover, the information flow between cortical regions may be facilitated by interregional rhythmic synchronization (5), where neuronal rhythms of specific different frequencies reflect the direction in which the information is flowing (6, 7). This bidirectional flow of information should also be a crucial feature of the neurobiological system that supports language processing. Linguistic processing is not a simple bottom-up process where incoming linguistic information (for instance, when reading a sentence) drives a sequence of activations of cortical areas that gradually transforms a string of letters into a representation of sentence and discourse meaning. Rather, contextual information, which is either already available, or built up while a sentence unfolds, can also provide top-down information, affecting the response in lower-order areas.Here, we show that interregional interactions in the human brain network for language are subserved by rhyt...

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“…Indeed, analysis of phase-topower interactions using GC on the simulated data always found an alpha phase to gamma power drive, irrespective of the true interactions (data not shown). This problem could potentially be alleviated using improved measures of GC (Vinck et al, 2015). Transfer entropy (Vicente et al, 2011), on the other hand, offers a principled way to quantify the directionality of interactions between different frequency bands (Besserve et al, 2010).…”

Section: Cross-frequency Directional Interactionsmentioning

confidence: 99%